This article provides a decision framework for choosing between fine-tuning, retrieval-augmented generation (RAG), and prompting for large language models. It clarifies that these techniques are not mutually exclusive and are often used in combination in sophisticated systems. The core of the decision process involves diagnosing the specific problem, such as a lack of knowledge, incorrect formatting, inappropriate tone, or deployment cost/latency issues, to determine the most effective approach. AI
IMPACT Provides a structured approach to optimize LLM implementation, potentially saving significant resources.
RANK_REASON The article presents a framework and analysis of different LLM techniques, fitting the definition of research/analysis.
Read on Medium — fine-tuning tag →
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